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Risk Score for Predicting In-Hospital Mortality in COVID-19 (RIM Score)

Infection by SARS-CoV2 has devastating consequences on health care systems. It is a global health priority to identify patients at risk of fatal outcomes. 1955 patients admitted to HM-Hospitales from 1 March to 10 June 2020 due to COVID-19, were were divided into two groups, 1310 belonged to the tra...

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Autores principales: López-Escobar, Alejandro, Madurga, Rodrigo, Castellano, José María, Velázquez, Sara, Suárez del Villar, Rafael, Menéndez, Justo, Peixoto, Alejandro, Jimeno, Sara, Ventura, Paula Sol, Ruiz de Aguiar, Santiago
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8065669/
https://www.ncbi.nlm.nih.gov/pubmed/33810534
http://dx.doi.org/10.3390/diagnostics11040596
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author López-Escobar, Alejandro
Madurga, Rodrigo
Castellano, José María
Velázquez, Sara
Suárez del Villar, Rafael
Menéndez, Justo
Peixoto, Alejandro
Jimeno, Sara
Ventura, Paula Sol
Ruiz de Aguiar, Santiago
author_facet López-Escobar, Alejandro
Madurga, Rodrigo
Castellano, José María
Velázquez, Sara
Suárez del Villar, Rafael
Menéndez, Justo
Peixoto, Alejandro
Jimeno, Sara
Ventura, Paula Sol
Ruiz de Aguiar, Santiago
author_sort López-Escobar, Alejandro
collection PubMed
description Infection by SARS-CoV2 has devastating consequences on health care systems. It is a global health priority to identify patients at risk of fatal outcomes. 1955 patients admitted to HM-Hospitales from 1 March to 10 June 2020 due to COVID-19, were were divided into two groups, 1310 belonged to the training cohort and 645 to validation cohort. Four different models were generated to predict in-hospital mortality. Following variables were included: age, sex, oxygen saturation, level of C-reactive-protein, neutrophil-to-platelet-ratio (NPR), neutrophil-to-lymphocyte-ratio (NLR) and the rate of changes of both hemogram ratios (VNLR and VNPR) during the first week after admission. The accuracy of the models in predicting in-hospital mortality were evaluated using the area under the receiver-operator-characteristic curve (AUC). AUC for models including NLR and NPR performed similarly in both cohorts: NLR 0.873 (95% CI: 0.849–0.898), NPR 0.875 (95% CI: 0.851–0.899) in training cohort and NLR 0.856 (95% CI: 0.818–0.895), NPR 0.863 (95% CI: 0.826–0.901) in validation cohort. AUC was 0.885 (95% CI: 0.885–0.919) for VNLR and 0.891 (95% CI: 0.861–0.922) for VNPR in the validation cohort. According to our results, models are useful in predicting in-hospital mortality risk due to COVID-19. The RIM Score proposed is a simple, widely available tool that can help identify patients at risk of fatal outcomes.
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spelling pubmed-80656692021-04-25 Risk Score for Predicting In-Hospital Mortality in COVID-19 (RIM Score) López-Escobar, Alejandro Madurga, Rodrigo Castellano, José María Velázquez, Sara Suárez del Villar, Rafael Menéndez, Justo Peixoto, Alejandro Jimeno, Sara Ventura, Paula Sol Ruiz de Aguiar, Santiago Diagnostics (Basel) Article Infection by SARS-CoV2 has devastating consequences on health care systems. It is a global health priority to identify patients at risk of fatal outcomes. 1955 patients admitted to HM-Hospitales from 1 March to 10 June 2020 due to COVID-19, were were divided into two groups, 1310 belonged to the training cohort and 645 to validation cohort. Four different models were generated to predict in-hospital mortality. Following variables were included: age, sex, oxygen saturation, level of C-reactive-protein, neutrophil-to-platelet-ratio (NPR), neutrophil-to-lymphocyte-ratio (NLR) and the rate of changes of both hemogram ratios (VNLR and VNPR) during the first week after admission. The accuracy of the models in predicting in-hospital mortality were evaluated using the area under the receiver-operator-characteristic curve (AUC). AUC for models including NLR and NPR performed similarly in both cohorts: NLR 0.873 (95% CI: 0.849–0.898), NPR 0.875 (95% CI: 0.851–0.899) in training cohort and NLR 0.856 (95% CI: 0.818–0.895), NPR 0.863 (95% CI: 0.826–0.901) in validation cohort. AUC was 0.885 (95% CI: 0.885–0.919) for VNLR and 0.891 (95% CI: 0.861–0.922) for VNPR in the validation cohort. According to our results, models are useful in predicting in-hospital mortality risk due to COVID-19. The RIM Score proposed is a simple, widely available tool that can help identify patients at risk of fatal outcomes. MDPI 2021-03-26 /pmc/articles/PMC8065669/ /pubmed/33810534 http://dx.doi.org/10.3390/diagnostics11040596 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Article
López-Escobar, Alejandro
Madurga, Rodrigo
Castellano, José María
Velázquez, Sara
Suárez del Villar, Rafael
Menéndez, Justo
Peixoto, Alejandro
Jimeno, Sara
Ventura, Paula Sol
Ruiz de Aguiar, Santiago
Risk Score for Predicting In-Hospital Mortality in COVID-19 (RIM Score)
title Risk Score for Predicting In-Hospital Mortality in COVID-19 (RIM Score)
title_full Risk Score for Predicting In-Hospital Mortality in COVID-19 (RIM Score)
title_fullStr Risk Score for Predicting In-Hospital Mortality in COVID-19 (RIM Score)
title_full_unstemmed Risk Score for Predicting In-Hospital Mortality in COVID-19 (RIM Score)
title_short Risk Score for Predicting In-Hospital Mortality in COVID-19 (RIM Score)
title_sort risk score for predicting in-hospital mortality in covid-19 (rim score)
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8065669/
https://www.ncbi.nlm.nih.gov/pubmed/33810534
http://dx.doi.org/10.3390/diagnostics11040596
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